@misc{10481/101706,
year = {2008},
url = {https://hdl.handle.net/10481/101706},
abstract = {We describe a neural network model of the cerebellum based on integrate-and-fire spiking neurons with conductance-based synapses. The neuron characteristics are derived from our earlier detailed models of the different cerebellar neurons. We tested the cerebellum model in a real-time control application with a robotic platform. Delays were introduced in the different sensorimotor pathways according to the biological system. The main plasticity in the cerebellar model is a spike-timing dependent plasticity (STDP) at the parallel fiber to Purkinje cell connections. This STDP is driven by the inferior olive (IO) activity, which encodes an error signal using a novel probabilistic low frequency model. We demonstrate the cerebellar model in a robot control system using a target-reaching task. We test whether the system learns to reach different target positions in a non-destructive way, therefore abstracting a general dynamics model. To test the system's ability to self-adapt to different dynamical situations, we present results obtained after changing the dynamics of the robotic platform significantly (its friction and load). The experimental results show that the cerebellar-based system is able to adapt dynamically to different contexts.},
keywords = {Spiking neuron},
keywords = {Cerebellum},
keywords = {Adaptive},
keywords = {Simulation},
keywords = {Learning},
keywords = {Inferior olive},
keywords = {Probabilistic},
keywords = {Robots},
keywords = {real time},
title = {A real-time spiking cerebellum model for learning robot control},
doi = {10.1016/j.biosystems.2008.05.008},
author = {Carrillo Sánchez, Richard Rafael and Ros Vidal, Eduardo and Boucheny, Christian and Coenen, Olivier J.-M. D.},
}